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Ng Details Key Steps for the Adoption of AI in Radiology

Ng Details Key Steps for the Adoption of AI in Radiology

May 31, 2019 -- BURLINGAME, CA - With the advance of artificial intelligence (AI) in medicine well underway, what's holding AI back from being deployed in clinical practice? AI luminary Andrew Ng, PhD, discussed several challenges to the process and the role radiologists can play in overcoming them during a keynote address delivered May 31 at the RSNA Spotlight Course: Radiology in the Age of AI.

Today we see the huge impact that AI is having in radiology research, but its clinical adoption appears to be slow, he told course attendees. Ng is a professor of computer science at Stanford University and CEO of Landing AI. He also served as director of the Google Brain deep-learning AI project and co-founded online learning platform Coursera.

"Progress in technology usually starts slowly, then moves very quickly. ... This is a common pattern in the adoption of AI, not just in healthcare," he said. "But once you get AI to work at a hospital somewhere, it will spread like wildfire across the world."

Today's Challenges for AI

"There is a rule of thumb that any task a person can do with one second of mental thought is a candidate for AI automation," Ng continued.

Indeed, numerous AI algorithms capable of performing radiology tasks are already in the works. For example, Ng and colleagues from Stanford University have developed various deep-learning algorithms that can detect up to 11 different pathologies on chest x-rays, spot abnormalities on knee MRI scans, and identify cerebral aneurysms on head CT angiograms -- all at a level comparable to that of radiologists.

Despite these developments, the integration of AI into everyday clinical practice can still feel slow, Ng noted. Why? He pointed to three major challenges facing clinicians today:

> Limited Data: Often referred to as the issue of "top 10 conditions," a limitation of many of the current AI algorithms in radiology is that they are only able to provide an accurate diagnosis of the most common medical conditions. Rare conditions have proven particularly difficult for AI to pinpoint due to the shortage of imaging data displaying them.

It is hard for AI to diagnose a rare condition based on just 10 images available in a medical textbook, Ng said. "Learning how to get algorithms to detect conditions on a limited number of images, or small data, is critical to breaking into the area of detecting rare conditions."

> Generalizability: A machine-learning model that works in the controlled environment described in a published paper frequently fails to work the same way in a production setting, he added. For imaging AI, algorithms are generally trained on a particular type of data, perhaps from a single institution, which leaves a huge gap between what worked in a specific research lab versus what will run in a different, real-life setting.

To emphasize this point, Ng demonstrated how effects as simple as glare or as extreme as the presence of unidentified objects in the background of a medical image diminished the ability of his team's XRay4All AI algorithm to diagnose pleural effusion on an x-ray.

"Machine-learning [algorithms] start out working well but gradually worsen over time because of natural changes in the environment," he said. "I'm incredibly optimistic about all of these algorithms, but these are things that we need to keep working on."

> Safety and Regulations: As with all innovations in healthcare, patient safety is paramount, he noted. Many regulations are in place to make sure that an AI algorithm does not cause harm. These regulations may delay the implementation of AI in medicine, yet they are nonetheless essential.